# Multi-Agent Training-free Urban Food Delivery System using Resilient UMST Network

arXiv: https://arxiv.org/abs/2604.03280 · HTML: https://mlsanity.com/paper/2604.03280/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [End-to-End Prediction of Parcel Delivery Time with Deep Learning for Smart-City Applications](https://mlsanity.com/paper/2009.12197/) (2020)
- [DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries](https://mlsanity.com/paper/2311.02017/) (2023)
- [PPtaxi: Non-stop Package Delivery via Multi-hop Ridesharing](https://mlsanity.com/paper/1809.04733/) (2018)

## Abstract (arXiv)

Delivery systems have become a core part of urban life, supporting the demand for food, medicine, and other goods. Yet traditional logistics networks remain fragile, often struggling to adapt to road closures, accidents, and shifting demand. Online Food Delivery (OFD) platforms now represent a cornerstone of urban logistics, with the global market projected to grow to over 500 billion USD by 2030. Designing delivery networks that are efficient and resilient remains a major challenge: fully connected graphs provide flexibility but are computationally infeasible at scale, while single Minimum Spanning Trees (MSTs) are efficient but easily disrupted. We propose the Union of Minimum Spanning Trees (UMST) approach to construct delivery networks that are sparse yet robust. UMST generates multiple MSTs through randomized edge perturbations and unites them, producing graphs with far fewer edges than fully connected networks while maintaining multiple alternative routes between delivery hotspots. Across multiple U.S. cities, UMST achieves 20--40× fewer edges than fully connected graphs while enabling substantial order bundling with 75--83% participation rates. Compared to learning-based baselines including MADDPG and Graph Neural Networks, UMST delivers competitive performance (88-96% success rates, 44-53% distance savings) without requiring training, achieving 30× faster execution while maintaining interpretable routing structures. Its combination of structural efficiency and operational flexibility offers a scalable and resilient foundation for urban delivery networks.